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Four similar truck travel-demand (TTD) models were developed for Wisconsin using 1993 Commodity Flow Survey (CFS) origin–destination (O-D) data and a limited number of truck classification count data. First, statewide zonal-level trip tables were developed using the CFS database. Then gravity models for four trip types were calibrated to match the trip-length frequency distributions of the CFS O-D trip tables. Finally, zonal trip productions and attractions were adjusted using an iterative procedure to match assigned traffic volumes with truck classification count data for 40 selected links. The four alternative TTD models differed only in the method used to assign external trips to the external stations. All of the models provided reasonable levels of goodness-of-fit to the 40 selected calibration links, as well as 104 additional count locations across the state. The primary limitation of the four models was that they substantially underestimated the truck trips that crossed the state boundary. The CFS data focused on longer-distance transport of major commodities; therefore, the local truck trips that used the external stations were underrepresented in the model. Simple regression models for estimating trip productions and attractions were developed using zonal population and employment as the independent variables. These improved models were possible because the CFS data made available more detailed initial estimates of trip productions and attractions.

Four similar truck travel-demand (TTD) models were developed for Wisconsin using 1993 Commodity Flow Survey (CFS) origin-destination (O-D) data and a limited number of truck classification count data. First, statewide zonal-level trip tables were developed using the CFS database. Then gravity models for four trip types were calibrated to match the trip-length frequency distributions of the CFS O-D trip tables. Finally, zonal trip productions and attractions were adjusted using an iterative procedure to match assigned traffic volumes with truck classification count data for 40 selected links. The four alternative TTD models differed only in the method used to assign external trips to the external stations. All of the models provided reasonable levels of goodness-of-fit to the 40 selected calibration links, as well as 104 additional count locations across the state. The primary limitation of the four models was that they substantially underestimated the truck trips that crossed the state boundary. The CFS data focused on longer-distance transport of major commodities; therefore, the local truck trips that used the external stations were underrepresented in the model. Simple regression models for estimating trip productions and attractions were developed using zonal population and employment as the independent variables. These improved models were possible because the CFS data made available more detailed initial estimates of trip productions and attractions.

A project was designed to enhance the ability of inspectors at fixed safety and weight stations (scales) to identify "out-of-service" (OOS) commercial vehicles and drivers by using advanced video-based license plate scanners linked to database software on a personal computer, the MOOSE system. In Wisconsin the results of safety inspections at the scales are stored in real time on a central computer database. This includes commercial vehicles and drivers placed OOS because of major safety violations. The primary goals were to increase the effectiveness of OOS enforcement efforts, establish a bistate enforcement program, and identify future applications. The technology was tested on a corridor involving three scales in Wisconsin and one in Minnesota on westbound I-90/I-94. The MOOSE system did identify a large number of OOS vehicles and drivers, but upon reinspection, almost no current OOS violations were found. The MOOSE system was successfully implemented at the Minnesota scale, but, as in Wisconsin, very few current OOS violations were identified. Because the Minnesota scale operates 24 hr/day, drivers coming from Wisconsin who are still OOS will probably use bypass routes. The greatest potential benefit from the MOOSE system is likely to be from linking the license plates to a new system that provides safety rating scores. Inspectors could then select vehicles for inspection that have a higher probability of being OOS or having other safety violations.

A project was designed to enhance the ability of inspectors at fixed safety and weight stations (scales) to identify “out-of-service” (OOS) commercial vehicles and drivers by using advanced video-based license plate scanners linked to database software on a personal computer, the MOOSE system. In Wisconsin the results of safety inspections at the scales are stored in real time on a central computer database. This includes commercial vehicles and drivers placed OOS because of major safety violations. The primary goals were to increase the effectiveness of OOS enforcement efforts, establish a bistate enforcement program, and identify future applications. The technology was tested on a corridor involving three scales in Wisconsin and one in Minnesota on westbound I-90/I-94. The MOOSE system did identify a large number of OOS vehicles and drivers, but upon reinspection, almost no current OOS violations were found. The MOOSE system was successfully implemented at the Minnesota scale, but, as in Wisconsin, very few current OOS violations were identified. Because the Minnesota scale operates 24 hr/day, drivers coming from Wisconsin who are still OOS will probably use bypass routes. The greatest potential benefit from the MOOSE system is likely to be from linking the license plates to a new system that provides safety rating scores. Inspectors could then select vehicles for inspection that have a higher probability of being OOS or having other safety violations.